MLOps combines “machine learning” and continuous software development operations and helps data scientists maintain and deploy ML models efficiently and responsibly.
Category: IT Professional
Let’s Architect! Architecting for Machine Learning
This blog post shows you how to design an MLOps pipeline for model monitoring to detect concept drift.
How to Run Massively Scalable ADAS Simulation Workloads on CAEdge
In this blog post, we’ll illustrate how the CAEdge Framework orchestrates ADAS simulation workloads with Amazon Managed Workflows for Apache Airflow (MWAA). We’ll show how it delegates the high-performance workloads to AWS Batch for elastic, highly scalable, and customizable compute needs.
How zero trust security will revolutionize DevSecOps
DevOps and DevSecOps have created controlled, easily deployable, secure and automated development-to-production processes that allow software updates multiple times per day with less risk.
Because enterprise network security relies on static firewall rules that can only be updated in maintenance windows after a change approval process, securely deploying applications in an automated way will not work in dynamic cloud environments
Shift left vs shift-right: A DevOps mystery solved
As part of the continuous cycle of progressive delivery, DevOps teams are also adopting shift-left and shift-right principles to ensure software quality in these dynamic environments.
Can You GitOps Your APIs?
Kusk (Swedish for coachman) automates the generation of ingress resources for various popular ingress controllers by treating your OpenAPI specification as the source of truth for their configuration.
Blockchains: Use Cases In Cybersecurity
We are starting to see alternative use cases emerge for blockchain technology, especially in areas such as cybersecurity identity management, food industry supply chains, healthcare, property records and digital voting.
Microservices assessment and readiness
You can use this guide to assess the maturity of your application, infrastructure, DevOps, development model, and more.
Cloud Native Continuous Delivery on Kubernetes with Tekton
The use case that we’ll be looking at is this, where we have parameters coming into this pipeline that has five paths, as a cloning, linting, testing, building, and then running task
Users can specify custom tasks, which are implemented by Kubernetes controllers that run on the cluster to provide functionality that’s not available directly in the Tekton pipelines API.
An Introductory Guide to Web, No-Code, and Low-Code App Builders – Solutions Review
An app builder allows you to build modern web, desktop, and mobile applications faster because they abstract away many of the complexities typically required for software development, specifically, the building of the front-end user interface.
Financial Crime Discovery using Amazon EKS and Graph Databases
We needed a solution that could scale and process millions of transactions, by effectively using high memory and CPU configurations to perform complex queries quickly
We used a graph database, Amazon Elastic Kubernetes Service (EKS), and Amazon Neptune, to search for suspicious financial chains across large amounts of transactional data in minutes.
How ENGIE scales their data ingestion pipelines using Amazon MWAA
In this post, we share how ENGIE created a CI/CD pipeline for an Amazon MWAA project template using an AWS CodeCommit repository and plugged it into AWS CodePipeline to build, test, and package the code and custom plugins.
How GE Aviation automated engine wash analytics with AWS Glue using a serverless architecture
This post demonstrates how we automated our engine wash analytics process to handle the complexity of ingesting data from multiple data sources and how we selected the right programming paradigm to reduce the overall time of the analytics job.
Build a modern data architecture on AWS with Amazon AppFlow, AWS Lake Formation, and Amazon Redshift: Part 2
The modern data platform ingests delta changes from all source data feeds once per night. The orchestration and transformations of the data is undertaken by dbt. dbt enables data analysts and engineers to write data transformation queries in a modular manner without having to maintain the run order manually.